David Ginsbourger

About

David Ginsbourger is a leading figure in the statistics of Gaussian processes and their application to engineering and robotics. His research focuses on the design and analysis of computer experiments, uncertainty quantification, and Bayesian optimization, with a particular emphasis on learning from demonstration and autonomous decision-making. Ginsbourger has made major contributions to the development of multi-output Gaussian process models, enabling robots to adapt their behavior by capturing both the variability and uncertainty inherent in human demonstrations. His work on learning excursion sets of vector-valued Gaussian random fields has advanced autonomous ocean sampling, allowing for more efficient and intelligent data collection in marine science. With key papers accumulating over 20 citations, his research bridges the gap between statistical theory and real-world robotics, offering powerful tools for adaptive control and experimental design. Ginsbourger’s notable achievements include pioneering methods that integrate model-based Gaussian processes into robotic learning, significantly improving how machines interpret and replicate complex tasks from limited human input.

Research Focus

Key Achievements

Top Papers

1. 1
   Learning from demonstration with model-based Gaussian process

   20 citations · 2019
2. 2
   Learning from demonstration with model-based Gaussian process

   5 citations · 2020
3. 3
   Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling

   3 citations · 2021

Key Collaborators

Contact & Links